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Slow movement small target detecting method base on frame correlation

机译:慢速运动小目标检测方法基于帧相关性

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For inter-frame motion characteristics of slowly moving small target, this paper is proposed a target detection algorithm based on relevant frames. This method use improved spatial high-pass filter for image pre-processing to improve signal to noise ratio of small targets; extract candidate target point by binary segmentation, and then conduct inter-frame correlation to binary image. Correlation is inter-frame corresponding to pixels in a neighborhood, and results stored in the tag matrix m, the matrix elements stand for the times of successful correlation. Here we use the rules of k/n rule to determine the output of the tag matrix, that is, if the n frames has k frames correlation successful, then the point is a candidate target. The advantage of this approch is: the short-term loss of target in the image sequence does not affect the detection effect, at the same time, overcome the high noise impact on small targets. Test results show that the method is a robust and efficient approach.
机译:对于缓慢移动小目标的帧间运动特性,提出了基于相关帧的目标检测算法。该方法使用改进的空间高通滤波器进行图像预处理,以提高小目标的信噪比;通过二进制分割提取候选目标点,然后与二进制图像进行帧间相关性。相关性是对应于邻域中的像素的帧间帧,并且存储在标签矩阵M中的结果,矩阵元素代表成功相关的时间。在这里,我们使用K / N规则的规则来确定标签矩阵的输出,即,如果n帧具有k帧相关性成功,则该点是候选目标。此代价的优点是:在图像序列中的靶的短期损失不会影响检测效果,同时克服对小目标的高噪声影响。测试结果表明,该方法是一种强大而有效的方法。

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